code: def test_smoothL1_ones(backend_default, fargs):
      s1, s2, m, sigma = fargs
      sigma2 = sigma**2
      shape = (s1, s2)
      outputs = np.ones(shape) * m
      targets = np.ones(shape) * m
      x = outputs - targets
      expected_result = np.zeros(shape)
      I1, J1 = np.where(abs(x) < 1.0 / sigma2)
      I2, J2 = np.where(abs(x) >= 1.0 / sigma2)
      expected_result[I1, J1] = 0.5 * x[I1, J1] ** 2 * sigma2
      expected_result[I2, J2] = abs(x[I2, J2]) - 0.5 / sigma2
      expected_result = np.sum(expected_result, axis=0, keepdims=True)
      compare_tensors(
          SmoothL1Loss(sigma=sigma),
          outputs,
          targets,
          expected_result,
          deriv=False,
          tol=1e-5,
      )
  
imports: import itertools
  import numpy
  from neon import NervanaObject
  from neon.backends import gen_backend
  from neon.transforms import CrossEntropyBinary, CrossEntropyMulti, SumSquared, MeanSquared, Misclassification, PrecisionRecall, SmoothL1Loss, SquareHingeLoss
label_desc: Use image node_id 21 for calling a global function with example usage: test_smoothL1_ones(backend_default, fargs) without return types